29 research outputs found
Decomposition strategies for large scale multi depot vehicle routing problems
Das Umfeld in der heutigen Wirtschaft verlangt nach immer bessern Ansätzen, um
Transportprobleme möglichst effizient zu lösen. Die Klasse der ”Vehicle Routing Problems” (VRP) beschäftigt sich speziell mit der Optimierung von Tourenplanungsproblemen
in dem ein Service-Leister seine Kunden möglichst effizient beliefern muss. Eine der VRP-Varianten ist das ”Multi Depot Vehicle Routing Problem with Time Windows” (MDVRPTW), in dem Kunden von verschiedenen Depots
in einem fix vorgegebenen Zeitintervall beliefert beliefert werden mĂĽssen. Das
MDVRPTW ist im realen Leben dank seiner realitätsnahen Restriktionen sehr oft
vertreten. Typische Transportprobleme, wie sie in der Wirklichkeit auftreten, sind
jedoch oftmals so groß, dass sie von optimalen Lösungsansätzen nicht zufriedenstellend
gelöst werden können.
In der vorliegenden Dissertation werden zwei Lösungsansätze präsentiert, wie
diese riesigen, realitätsnahen Probleme zufriedenstellend bewältigt werden können.
Beide Ansätze benutzen die POPMUSIC Grundstruktur, um das Problem möglichst
intelligent zu dekomponieren. Die Dekomponierten und damit kleineren Subprobleme
können dann von speziell entwickelten Algorithmen effizienter bearbeitet
und letztendlich gelöst werden. Mit dem ersten Ansatz präsentieren wir
eine Möglichkeit Transportprobleme zu dekomponieren, wenn populationsbasierte
Algorithmen als Problemlöser eingesetzt werden. Dazu wurde ein maßgeschneiderter
Memetischer Algorithmus (MA) entwickelt und in das Dekompositionsgerüst eingebaut um ein reales Problem eines österreichischen Transportunternehmens
zu lösen. Wir zeigen, dass die Dekomponierung und Optimierung
der resultierenden Subprobleme, im Vergleich zu den Ergebnissen des MA ohne
Dekomposition, eine Verbesserung der Zielfunktion von rund 20% ermöglicht.
Der zweite Ansatz beschäftigt sich mit der Entwicklung einer Dekomponierungsmethode
für Lösungsalgorithmen, die nur an einer einzigen Lösung arbeiten. Es wurde ein ”Variable Neigborhood Search” (VNS) als Optimierer in das POPMUSIC
GrundgerĂĽst implementiert, um an das vorhandene Echtwelt-Problem heranzugehen.
Wir zeigen, dass dieser Ansatz rund 7% bessere Ergebnisse liefert als
der pure VNS Lösungsansatz. Außerdem präsentieren wir Ergebnisse des VNS
Dekompositionsansatzes die um rund 6% besser sind als die des MA Dekompositionsansatzes.
Ein weiterer Beitrag dieser Arbeit ist das Vorstellen von zwei komplett verschiedenen
Ansätzen um das Problem in kleinere Sub-Probleme zu zerteilen. Dazu
wurden acht verschiedene Nähe-Maße definiert und betrachtet. Es wurde der
2,3 und 4 Depot Fall getestet und im Detail analysiert. Die Ergebnisse werden
präsentiert und wir stellen einen eindeutigen Gewinner vor, der alle Testinstanzen
am Besten lösen konnte. Wir weisen auch darauf hin, wie einfach die POPMUSIC
Dekomponierung an reale Bedürfnisse, wie zum Beispiel eine möglichst
schnelle Ergebnisgenerierung, angepasst werden kann. Wir zeigen damit, dass
die vorgestellten Dekomponierungsstrategien sehr effizient und flexibel sind, wenn
Transportprobleme, wie sie in der realen Welt vorkommen gelöst werden müssen.The optimization of transportation activities is of high importance for companies
in today’s economy. The Vehicle Routing Problem (VRP) class is dealing with
the routing of vehicles so that the customer base of a company can be served
in the most efficient way. One of the many variants in the VRP class is the
Multi Depot Vehicle Routing Problem with Time Windows (MDVRPTW) which
extends the VRP by additional depots from which customers can be served, as
well as an individual time window for each customer in which he is allowed to
be served. Modern carrier fleet operators often encounter these MDVRPTW in
the real world, and usually they are of very large size so that exact approaches
cannot solve them efficiently. This thesis presents two different approaches how
this real world large scale MDVRPTWs can be solved. Both approaches are based
on the POPMUSIC framework, which intelligently tries to decompose the large
scale problem into much smaller sub-problems. The resulting sub-problems can
then be solved more efficiently by specialized optimizers. The first approach in
this thesis was developed for population based optimizers. A Memetic Algorithm
(MA) was developed and used as an optimizer in the framework to solve a real
world MDVPRTW from an Austrian carrier fleet operator. We show that decomposing
the complete problem and solving the resulting sub-problems improves the
solution quality by around 20% compared to using the MA without any decomposition.
The second approach specially focuses on decomposition strategies for
single solution methods. More precisely, a Variable Neighborhood Search (VNS)
was implemented in the POPMUSIC framework to solve the real world instances.
We show that decomposing the problem can yield improvements of around 7%
compared to using the pure VNS method. Compared to the POPMUSIC MA
approach the second approach can further improve the solution quality by around
6%. Another contribution in this thesis is the development of two generally different ways to measure proximity when creating sub-problems. In detail we tested
eight different proximity measures and analyzed how good they decompose the
problem in different environments. We tested the two, three and four depot case
and present a clear winner that can outperform all other measures. Further we
demonstrate that the POPMUSIC approach can flexibly be adjusted to real world
demands, like a faster solution finding process, while at the same time maintaining
high quality solutions. We show that a decomposition strategies combined with
state of the art metaheuristic solvers are a very efficient and flexible tool to tackle
real world problems with regards to solution quality as well as runtime
Internet of Things in urban waste collection
Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving
Design of Heuristic Algorithms for Hard Optimization
This open access book demonstrates all the steps required to design heuristic algorithms for difficult optimization. The classic problem of the travelling salesman is used as a common thread to illustrate all the techniques discussed. This problem is ideal for introducing readers to the subject because it is very intuitive and its solutions can be graphically represented. The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods. Each metaheuristic can then be presented in simplified form as a combination of these concepts. This approach avoids giving the impression that metaheuristics is a non-formal discipline, a kind of cloud sculpture. Moreover, it provides concrete applications of the travelling salesman problem, which illustrate in just a few lines of code how to design a new heuristic and remove all ambiguities left by a general framework. Two chapters reviewing the basics of combinatorial optimization and complexity theory make the book self-contained. As such, even readers with a very limited background in the field will be able to follow all the content
Multi-Depot and Multi-School Bus Scheduling Problem with School Bell Time Optimization
69A43551747123The school bus transportation system is responsible for transporting students to and from schools safely and promptly. This research aims to optimize the school bus schedules and the school bell times simultaneously for improving the efficiency of the school bus system operation. The authors consider the school bus scheduling problem in a multi-depot multi-school bus system and incorporate the bell time optimization to make bus operations more efficient. The authors propose four different methods, including one exact method and three heuristic methods, to solve the Multi-depot and Multi-school Bus Scheduling Problem with School Bell Time Optimization (MDSBSPTW)
Machine learning into metaheuristics: A survey and taxonomy of data-driven metaheuristics
During the last years, research in applying machine learning (ML) to design efficient, effective and robust metaheuristics became increasingly popular. Many of those data driven metaheuristics have generated high quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this paper we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies which might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem, low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic which needs further in-depth investigations
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations
In recent years, a great variety of nature- and bio-inspired algorithms has
been reported in the literature. This algorithmic family simulates different
biological processes observed in Nature in order to efficiently address complex
optimization problems. In the last years the number of bio-inspired
optimization approaches in literature has grown considerably, reaching
unprecedented levels that dark the future prospects of this field of research.
This paper addresses this problem by proposing two comprehensive,
principle-based taxonomies that allow researchers to organize existing and
future algorithmic developments into well-defined categories, considering two
different criteria: the source of inspiration and the behavior of each
algorithm. Using these taxonomies we review more than three hundred
publications dealing with nature-inspired and bio-inspired algorithms, and
proposals falling within each of these categories are examined, leading to a
critical summary of design trends and similarities between them, and the
identification of the most similar classical algorithm for each reviewed paper.
From our analysis we conclude that a poor relationship is often found between
the natural inspiration of an algorithm and its behavior. Furthermore,
similarities in terms of behavior between different algorithms are greater than
what is claimed in their public disclosure: specifically, we show that more
than one-third of the reviewed bio-inspired solvers are versions of classical
algorithms. Grounded on the conclusions of our critical analysis, we give
several recommendations and points of improvement for better methodological
practices in this active and growing research field.Comment: 76 pages, 6 figure
Models, methods and algorithms for supply chain planning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.An outline of supply chains and differences in the problem types is given. The motivation for a generic framework is discussed and explored. A conceptual model is presented along with it application to real world situations; and from this a database model is developed.
A MIP and CP implementations are presented; along with alternative formulation which can be use to solve the problems. A local search solution algorithm is presented and shown to have significant benefits.
Problem instances are presented which are used to validate the generic models, including a large manufacture and distribution problem. This larger problem instance is not only used to explore the implementation of the models presented, but also to explore the practically of the use of alternative formulation and solving techniques within the generic framework and the effectiveness of such methods including the neighbourhood search solving method.
A stochastic dimension to the generic framework is explored, and solution techniques for this extension are explored, demonstrating the use of solution analysis to allow problem simplification and better solutions to be found. Finally the local search algorithm is applied to the larger models that arise from inclusion of scenarios, and the methods is demonstrated to be powerful for finding solutions for these large model that were insoluble using the MIP on the same hardware
Innovative Hybrid Approaches for Vehicle Routing Problems
This thesis deals with the efficient resolution of Vehicle Routing Problems (VRPs).
The first chapter faces the archetype of all VRPs: the Capacitated Vehicle Routing Problem (CVRP). Despite having being introduced more than 60 years ago, it still remains an extremely challenging problem. In this chapter I design a Fast Iterated-Local-Search Localized Optimization algorithm for the CVRP, shortened to FILO. The simplicity of the CVRP definition allowed me to experiment with advanced local search acceleration and pruning techniques that have eventually became the core optimization engine of FILO. FILO experimentally shown to be extremely scalable and able to solve very large scale instances of the CVRP in a fraction of the computing time compared to existing state-of-the-art methods, still obtaining competitive solutions in terms of their quality.
The second chapter deals with an extension of the CVRP called the Extended Single Truck and Trailer Vehicle Routing Problem, or simply XSTTRP. The XSTTRP models a broad class of VRPs in which a single vehicle, composed of a truck and a detachable trailer, has to serve a set of customers with accessibility constraints making some of them not reachable by using the entire vehicle. This problem moves towards VRPs including more realistic constraints and it models scenarios such as parcel deliveries in crowded city centers or rural areas, where maneuvering a large vehicle is forbidden or dangerous. The XSTTRP generalizes several well known VRPs such as the Multiple Depot VRP and the Location Routing Problem. For its solution I developed an hybrid metaheuristic which combines a fast heuristic optimization with a polishing phase based on the resolution of a limited set partitioning problem. Finally, the thesis includes a final chapter aimed at guiding the computational evaluation of new approaches to VRPs proposed by the machine learning community
Berth Scheduling at Seaports: Meta-Heuristics and Simulation
This research aims to develop realistic solutions to enhance the efficiency of port operations. By conducting a comprehensive literature review on logistic problems at seaports, some important gaps have been identified for the first time. The following contributions are made in order to close some of the existing gaps. Firstly, this thesis identifies important realistic features which have not been well-studied in current academic research of berth planning. This thesis then aims to solve a discrete dynamic Berth allocation problem (BAP) while taking tidal constraints into account. As an important feature when dealing with realistic scheduling, changing tides have not been well-considered in BAPs. To the best of our knowledge, there is no existing work using meta-heuristics to tackle the BAP with multiple tides that can provide feasible solutions for all the test cases. We propose one single-point meta-heuristic and one population-based meta-heuristic. With our algorithms, we meet the following goals: (i) to minimise the cost of all vessels while staying in the port, and (ii) to schedule available berths for the arriving vessels taking into account a multi-tidal planning horizon. Comprehensive experiments are conducted in order to analyse the strengths and weaknesses of the algorithms and compare with both exact and approximate methods. Furthermore, lacking tools for examining existing algorithms for different optimisation problems and simulating real-world scenarios is identified as another gap in this study. This thesis develops a discrete-event simulation framework. The framework is able to generate test cases for different problems and provide visualisations. With this framework, contributions include assessing the performance of different algorithms for optimisation problems and benchmarking optimisation problems